The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings
Abstract
:1. Introduction
2. Deep Support Vector Data Description
3. The Robust Multi-Scale Deep-SVDD Model of Incipient Fault Detection
3.1. Signal Enhancement
3.1.1. Horizontal Scaling
3.1.2. Vertical Scaling
3.2. Prototype Clustering
Algorithm 1 Learning Vector Quantization | |
Input: | Sample set ; Suppose that, is the number of prototype vectors, is the initial category of each prototype vector, and is the learning rate. |
Ouput: | prototype vector |
1: | Initialize the prototype vector . |
2: | Loop |
3: | Select samples from sample set randomly. |
4: | Calculate the distance between and : |
5: | Find the prototype vector closest to , |
6: | If |
7: | |
8: | Else |
9: | |
10: | End if |
11: | Update to |
12: | Until the stop criterian is reached |
3.3. Distance-Based Cross Entropy Loss
3.4. Robust Multi-Scale Deep-SVDD
3.5. Calculation of Anomaly Score
4. Experiment
4.1. Dataset Introduction
4.2. Model Parameter Settings
4.3. Incipient Fault Detection Results
4.4. Comparative Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Sample | Number of Sample | Training Sample | Testing Sample | The Real Sample Point of Incipient Fault | Number of Early Fault Samples |
---|---|---|---|---|---|---|
IEEE PHM Challenge 2012 dataset | Condition 1 Bearing1_2 | 871 | The first 100 samples | The rest 771 samples | - | 479 |
Condition 1 Bearing1_3 | 2375 | The first 100 samples | The rest 2275 samples | 1348th | 1027 | |
XJTU-SY dataset | Condition 1 Bearing1_1 | 1476 | The first 100 samples | The rest 1376 samples | 634th | 839 |
Condition 2 Bearing2_2 | 1932 | The first 100 samples | The rest 1832 samples | - | 942 |
Comparison Methods | PHM1_3 | XJTU1_1 | ||
---|---|---|---|---|
The Detected Sample Point | Deviation Rate of Incipient Fault Detection | The Detected Sample Point | Deviation Rate of Incipient Fault Detection | |
1. BEMD-AMMA | 1600 | 55.79% | 1320 | 57.33% |
2. LOF | 1236 | 20.35% | 944 | 12.51% |
3. iFOREST | 1341 | 30.57% | 1041 | 24.08% |
4. SDFM | 1156 | 12.56% | 1137 | 35.52% |
5. SRD | 1160 | 12.95% | 1013 | 20.74% |
6. The proposed method | 997 | 2.9% | 826 | 1.55% |
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Kou, L.; Chen, J.; Qin, Y.; Mao, W. The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings. Sensors 2022, 22, 5681. https://doi.org/10.3390/s22155681
Kou L, Chen J, Qin Y, Mao W. The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings. Sensors. 2022; 22(15):5681. https://doi.org/10.3390/s22155681
Chicago/Turabian StyleKou, Linlin, Jiaxian Chen, Yong Qin, and Wentao Mao. 2022. "The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings" Sensors 22, no. 15: 5681. https://doi.org/10.3390/s22155681
APA StyleKou, L., Chen, J., Qin, Y., & Mao, W. (2022). The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings. Sensors, 22(15), 5681. https://doi.org/10.3390/s22155681